OpenRaiser/Intern-Atlas huggingface dataset:
Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration
Venue: arXiv cs.CV 2024
Year: 2024

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs.
A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model
Venue: arXiv cs.SE 2024
Year: 2024

The rapid advancement of AI technology has led to widespread applications of agent systems across various domains. However, the need for detailed architecture design poses significant challenges in designing and operating these systems. This paper introduces a taxonomy focused on the architectures of foundation-model-based agents, addressing critical aspects such as functional capabilities and non-functional qualities. We also discuss the operations involved in both design-time and run-time phases, providing a comprehensive view of architectural design and operational characteristics. By unifying and detailing these classifications, our taxonomy aims to improve the design of foundation-model-based agents. Additionally, the paper establishes a decision model that guides critical design and runtime decisions, offering a structured approach to enhance the development of foundation-model-based agents. Our contributions include providing a structured architecture design option and guiding the development process of foundation-model-based agents, thereby addressing current fragmentation in the field.
Segmenting Small Stroke Lesions with Novel Labeling Strategies
Venue: arXiv cs.CV 2024
Year: 2024

Deep neural networks have demonstrated exceptional efficacy in stroke lesion segmentation. However, the delineation of small lesions, critical for stroke diagnosis, remains a challenge. In this study, we propose two straightforward yet powerful approaches that can be seamlessly integrated into a variety of networks: Multi-Size Labeling (MSL) and Distance-Based Labeling (DBL), with the aim of enhancing the segmentation accuracy of small lesions. MSL divides lesion masks into various categories based on lesion volume while DBL emphasizes the lesion boundaries. Experimental evaluations on the Anatomical Tracings of Lesions After Stroke (ATLAS) v2.0 dataset showcase that an ensemble of MSL and DBL achieves consistently better or equal performance on recall (3.6% and 3.7%), F1 (2.4% and 1.5%), and Dice scores (1.3% and 0.0%) compared to the top-1 winner of the 2022 MICCAI ATLAS Challenge on both the subset only containing small lesions and the entire dataset, respectively. Notably, on the mini-lesion subset, a single MSL model surpasses the previous best ensemble strategy, with enhancements of 1.0% and 0.3% on F1 and Dice scores, respectively. Our code is available at: https://github.com/nadluru/StrokeLesSeg.
Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping
Venue: arXiv cs.LG 2024
Year: 2024

Clustering is a fundamental task in machine learning and data science, and similarity graph-based clustering is an important approach within this domain. Doubly stochastic symmetric similarity graphs provide numerous benefits for clustering problems and downstream tasks, yet learning such graphs remains a significant challenge. Marcus theorem states that a strictly positive symmetric matrix can be transformed into a doubly stochastic symmetric matrix by diagonal matrices. However, in clustering, learning sparse matrices is crucial for computational efficiency. We extend Marcus theorem by proposing the Marcus mapping, which indicates that certain sparse matrices can also be transformed into doubly stochastic symmetric matrices via diagonal matrices. Additionally, we introduce rank constraints into the clustering problem and propose the Doubly Stochastic Adaptive Neighbors Clustering algorithm based on the Marcus Mapping (ANCMM). This ensures that the learned graph naturally divides into the desired number of clusters. We validate the effectiveness of our algorithm through extensive comparisons with state-of-the-art algorithms. Finally, we explore the relationship between the Marcus mapping and optimal transport. We prove that the Marcus mapping solves a specific type of optimal transport problem and demonstrate that solving this problem through Marcus mapping is more efficient than directly applying optimal transport methods.
Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application
Venue: arXiv cs.LG 2024
Year: 2024

Federated learning has become an emerging technology for data analysis for IoT applications. This paper implements centralized and decentralized federated learning frameworks for crop yield prediction based on Long Short-Term Memory Network. For centralized federated learning, multiple clients and one server is considered, where the clients exchange their model updates with the server that works as the aggregator to build the global model. For the decentralized framework, a collaborative network is formed among the devices either using ring topology or using mesh topology. In this network, each device receives model updates from the neighbour devices, and performs aggregation to build the upgraded model. The performance of the centralized and decentralized federated learning frameworks are evaluated in terms of prediction accuracy, precision, recall, F1-Score, and training time. The experimental results present that $\geq$97% and $>$97.5% prediction accuracy are achieved using the centralized and decentralized federated learning-based frameworks respectively. The results also show that the using centralized federated learning the response time can be reduced by $\sim$75% than the cloud-only framework. Finally, the future research directions of the use of federated learning in crop yield prediction are explored in this paper.
Automatic String Data Validation with Pattern Discovery
Venue: arXiv cs.DB 2024
Year: 2024

In enterprise data pipelines, data insertions occur periodically and may impact downstream services if data quality issues are not addressed. Typically, such problems can be investigated and fixed by on-call engineers, but locating the cause of such problems and fixing errors are often time-consuming. Therefore, automatic data validation is a better solution to defend the system and downstream services by enabling early detection of errors and providing detailed error messages for quick resolution. This paper proposes a self-validate data management system with automatic pattern discovery techniques to verify the correctness of semi-structural string data in enterprise data pipelines. Our solution extracts patterns from historical data and detects erroneous incoming data in a top-down fashion. High-level information of historical data is analyzed to discover the format skeleton of correct values. Fine-grained semantic patterns are then extracted to strike a balance between generalization and specification of the discovered pattern, thus covering as many correct values as possible while avoiding over-fitting. To tackle cold start and rapid data growth, we propose an incremental update strategy and example generalization strategy. Experiments on large-scale industrial and public datasets demonstrate the effectiveness and efficiency of our method compared to alternative solutions. Furthermore, a case study on an industrial platform (Ant Group Inc.) with thousands of applications shows that our system captures meaningful data patterns in daily operations and helps engineers quickly identify errors.
Congestion or No Congestion: Packet Loss Identification and Prediction Using Machine Learning
Venue: arXiv cs.NI 2024
Year: 2024

Packet losses in the network significantly impact network performance. Most TCP variants reduce the transmission rate when detecting packet losses, assuming network congestion, resulting in lower throughput and affecting bandwidth-intensive applications like immersive applications. However, not all packet losses are due to congestion; some occur due to wireless link issues, which we refer to as non-congestive packet losses. In today's hybrid Internet, packets of a single flow may traverse wired and wireless segments of a network to reach their destination. TCP should not react to non-congestive packet losses the same way as it does to congestive losses. However, TCP currently can not differentiate between these types of packet losses and lowers its transmission rate irrespective of packet loss type, resulting in lower throughput for wireless clients. To address this challenge, we use machine learning techniques to distinguish between these types of packet losses at end hosts, utilizing easily available features at the host. Our results demonstrate that Random Forest and K-Nearest Neighbor classifiers perform better in predicting the type of packet loss, offering a promising solution to enhance network performance.
Closed-Loop Magnetic Control of Medical Soft Continuum Robots for Deflection
Venue: arXiv cs.RO 2024
Year: 2024

Magnetic soft continuum robots (MSCRs) have emerged as powerful devices in endovascular interventions owing to their hyperelastic fibre matrix and enhanced magnetic manipulability. Effective closed-loop control of tethered magnetic devices contributes to the achievement of autonomous vascular robotic surgery. In this article, we employ a magnetic actuation system equipped with a single rotatable permanent magnet to achieve closed-loop deflection control of the MSCR. To this end, we establish a differential kinematic model of MSCRs exposed to non-uniform magnetic fields. The relationship between the existence and uniqueness of Jacobian and the geometric position between robots is deduced. The control direction induced by Jacobian is demonstrated to be crucial in simulations. Then, the corresponding quasi-static control (QSC) framework integrates a linear extended state observer to estimate model uncertainties. Finally, the effectiveness of the proposed QSC framework is validated through comparative trajectory tracking experiments with the PD controller under external disturbances. Further extensions are made for the Jacobian to path-following control at the distal end position. The proposed control framework prevents the actuator from reaching the joint limit and achieves fast and low error-tracking performance without overshooting.
The Crowd in MOOCs: A Study of Learning Patterns at Scale
Venue: arXiv cs.IR 2024
Year: 2024

The increasing availability of learning activity data in Massive Open Online Courses (MOOCs) enables us to conduct a large-scale analysis of learners' learning behavior. In this paper, we analyze a dataset of 351 million learning activities from 0.8 million unique learners enrolled in over 1.6 thousand courses within two years. Specifically, we mine and identify the learning patterns of the crowd from both temporal and course enrollment perspectives leveraging mutual information theory and sequential pattern mining methods. From the temporal perspective, we find that the time intervals between consecutive learning activities of learners exhibit a mix of power-law and periodic cosine function distribution. By qualifying the relationship between course pairs, we observe that the most frequently co-enrolled courses usually fall in the same category or the same university. We demonstrate these findings can facilitate manifold applications including recommendation tasks on courses. A simple recommendation model utilizing the course enrollment patterns is competitive to the baselines with 200$\times$ faster training time.
Towards an Analysis of Discourse and Interactional Pragmatic Reasoning Capabilities of Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

In this work, we want to give an overview on which pragmatic abilities have been tested in LLMs so far and how these tests have been carried out. To do this, we first discuss the scope of the field of pragmatics and suggest a subdivision into discourse pragmatics and interactional pragmatics. We give a non-exhaustive overview of the phenomena of those two subdomains and the methods traditionally used to analyze them. We subsequently consider the resulting heterogeneous set of phenomena and methods as a starting point for our survey of work on discourse pragmatics and interactional pragmatics in the context of LLMs.
Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning
Venue: arXiv cs.LG 2024
Year: 2024

The behavior decision-making subsystem is a key component of the autonomous driving system, which reflects the decision-making ability of the vehicle and the driver, and is an important symbol of the high-level intelligence of the vehicle. However, the existing rule-based decision-making schemes are limited by the prior knowledge of designers, and it is difficult to cope with complex and changeable traffic scenarios. In this work, an advanced deep reinforcement learning model is adopted, which can autonomously learn and optimize driving strategies in a complex and changeable traffic environment by modeling the driving decision-making process as a reinforcement learning problem. Specifically, we used Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) for comparative experiments. DQN guides the agent to choose the best action by approximating the state-action value function, while PPO improves the decision-making quality by optimizing the policy function. We also introduce improvements in the design of the reward function to promote the robustness and adaptability of the model in real-world driving situations. Experimental results show that the decision-making strategy based on deep reinforcement learning has better performance than the traditional rule-based method in a variety of driving tasks.
Lisbon Computational Linguists at SemEval-2024 Task 2: Using A Mistral 7B Model and Data Augmentation
Venue: arXiv cs.CL 2024
Year: 2024

This paper describes our approach to the SemEval-2024 safe biomedical Natural Language Inference for Clinical Trials (NLI4CT) task, which concerns classifying statements about Clinical Trial Reports (CTRs). We explored the capabilities of Mistral-7B, a generalist open-source Large Language Model (LLM). We developed a prompt for the NLI4CT task, and fine-tuned a quantized version of the model using an augmented version of the training dataset. The experimental results show that this approach can produce notable results in terms of the macro F1-score, while having limitations in terms of faithfulness and consistency. All the developed code is publicly available on a GitHub repository
The Dawn of Decentralized Social Media: An Exploration of Bluesky's Public Opening
Venue: arXiv cs.SI 2024
Year: 2024

Bluesky is a Twitter-like decentralized social media platform that has recently grown in popularity. After an invite-only period, it opened to the public worldwide on February 6th, 2024. In this paper, we provide a longitudinal analysis of user activity in the two months around the opening, studying changes in the general characteristics of the platform due to the rapid growth of the user base. We observe a broad distribution of activity similar to more established platforms, but a higher volume of original than reshared content, and very low toxicity. After opening to the public, Bluesky experienced a large surge in new users and activity, especially posting English and Japanese content. In particular, several accounts entered the discussion with suspicious behavior, like following many accounts and sharing content from low-credibility news outlets. Some of these have already been classified as spam or suspended, suggesting effective moderation.
Fake News Detection via Wisdom of Synthetic & Representative Crowds
Venue: arXiv cs.CY 2024
Year: 2024

Social media companies have struggled to provide a democratically legitimate definition of "Fake News". Reliance on expert judgment has attracted criticism due to a general trust deficit and political polarisation. Approaches reliant on the ``wisdom of the crowds'' are a cost-effective, transparent and inclusive alternative. This paper provides a novel end-to-end methodology to detect fake news on X via "wisdom of the synthetic & representative crowds". We deploy an online survey on the Lucid platform to gather veracity assessments for a number of pandemic-related tweets from crowd-workers. Borrowing from the MrP literature, we train a Hierarchical Bayesian model to predict the veracity of each tweet from the perspective of different personae from the population of interest. We then weight the predicted veracity assessments according to a representative stratification frame, such that decisions about ``fake'' tweets are representative of the overall polity of interest. Based on these aggregated scores, we analyse a corpus of tweets and perform a second MrP to generate state-level estimates of the number of people who share fake news. We find small but statistically meaningful heterogeneity in fake news sharing across US states. At the individual-level: i. sharing fake news is generally rare, with an average sharing probability interval [0.07,0.14]; ii. strong evidence that Democrats share less fake news, accounting for a reduction in the sharing odds of [57.3%,3.9%] relative to the average user; iii. when Republican definitions of fake news are used, it is the latter who show a decrease in the propensity to share fake news worth [50.8%, 2.0%]; iv. some evidence that women share less fake news than men, an effect worth a [29.5%,4.9%] decrease.
CADRL: Category-aware Dual-agent Reinforcement Learning for Explainable Recommendations over Knowledge Graphs
Venue: arXiv cs.IR 2024
Year: 2024

Knowledge graphs (KGs) have been widely adopted to mitigate data sparsity and address cold-start issues in recommender systems. While existing KGs-based recommendation methods can predict user preferences and demands, they fall short in generating explicit recommendation paths and lack explainability. As a step beyond the above methods, recent advancements utilize reinforcement learning (RL) to find suitable items for a given user via explainable recommendation paths. However, the performance of these solutions is still limited by the following two points. (1) Lack of ability to capture contextual dependencies from neighboring information. (2) The excessive reliance on short recommendation paths due to efficiency concerns. To surmount these challenges, we propose a category-aware dual-agent reinforcement learning (CADRL) model for explainable recommendations over KGs. Specifically, our model comprises two components: (1) a category-aware gated graph neural network that jointly captures context-aware item representations from neighboring entities and categories, and (2) a dual-agent RL framework where two agents efficiently traverse long paths to search for suitable items. Finally, experimental results show that CADRL outperforms state-of-the-art models in terms of both effectiveness and efficiency on large-scale datasets.
typedKanren: Statically Typed Relational Programming with Exhaustive Matching in Haskell
Venue: arXiv cs.PL 2024
Year: 2024

We present a statically typed embedding of relational programming (specifically a dialect of miniKanren with disequality constraints) in Haskell. Apart from handling types, our dialect extends standard relational combinator repertoire with a variation of relational matching that supports static exhaustiveness checks. To hide the boilerplate definitions and support comfortable logic programming with user-defined data types we use generic programming via GHC.Generics as well as metaprogramming via Template Haskell. We demonstrate our dialect on several examples and compare its performance against some other known implementations of miniKanren.
Employing Vector Field Techniques on the Analysis of Memristor Cellular Nonlinear Networks Cell Dynamics
Venue: arXiv cs.ET 2024
Year: 2024

This paper introduces an innovative graphical analysis tool for investigating the dynamics of Memristor Cellular Nonlinear Networks (M-CNNs) featuring 2nd-order processing elements, known as M-CNN cells. In the era of specialized hardware catering to the demands of intelligent autonomous systems, the integration of memristors within Cellular Nonlinear Networks (CNNs) has emerged as a promising paradigm due to their exceptional characteristics. However, the standard Dynamic Route Map (DRM) analysis, applicable to 1st-order systems, fails to address the intricacies of 2nd-order M-CNN cell dynamics, as well the 2nd-order DRM (DRM2) exhibits limitations on the graphical illustration of local dynamical properties of the M-CNN cells, e.g. state derivative's magnitude. To address this limitation, we propose a novel integration of M-CNN cell vector field into the cell's phase portrait, enhancing the analysis efficacy and enabling efficient M-CNN cell design. A comprehensive exploration of M-CNN cell dynamics is presented, showcasing the utility of the proposed graphical tool for various scenarios, including bistable and monostable behavior, and demonstrating its superior ability to reveal subtle variations in cell behavior. Through this work, we offer a refined perspective on the analysis and design of M-CNNs, paving the way for advanced applications in edge computing and specialized hardware.
JetUnit: Rendering Diverse Force Feedback in Virtual Reality Using Water Jets
Venue: arXiv cs.HC 2024
Year: 2024

We propose JetUnit, a water-based VR haptic system designed to produce force feedback with a wide spectrum of intensities and frequencies through water jets. The key challenge in designing this system lies in optimizing parameters to enable the haptic device to generate force feedback that closely replicates the most intense force produced by direct water jets while ensuring the user remains dry. In this paper, we present the key design parameters of the JetUnit wearable device determined through a set of quantitative experiments and a perception study. We further conducted a user study to assess the impact of integrating our haptic solutions into virtual reality experiences. The results revealed that, by adhering to the design principles of JetUnit, the water-based haptic system is capable of delivering diverse force feedback sensations, significantly enhancing the immersive experience in virtual reality.